PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and TCGA Pathology
Multimodal medical prediction often faces incomplete pairing: auxiliary modalities with complementary signal are available for only a subset of subjects (or none) and cannot be assumed at deployment. We introduce PANDA (Prototype Anchored Data Alignment), a two-stage framework that transfers auxiliary information to a primary-modality model without auxiliary inputs at inference. Stage 1 learns a shared embedding from the paired subset and estimates class prototypes from auxiliary modalities; Stage 2 trains the primary encoder on all subjects using cross-entropy plus alignment to the frozen pro
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- PossiblePossibly related (embedding) · 46%Introducing Gemma 4 12B: a unified, encoder-free multimodal model →
- LinkedLinked via arxiv author · 85%Sheethal Bhat →
“PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and”
- LinkedLinked via arxiv author · 85%Mahfuzur Rahman Chowdhury →
“PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and”
- LinkedLinked via arxiv author · 85%Paula Andrea Perez-Toro →
“PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and”
- LinkedLinked via arxiv author · 85%Stephan Wunderlich →
“PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and”
- LinkedLinked via arxiv author · 85%Rose Dawn Bharat →
“PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and”
- LinkedLinked via arxiv author · 85%Siming Bayer →
“PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and”
- LinkedLinked via arxiv author · 85%Andreas Maier →
“PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and”